SDE IV - Data Engineer

• Data Stack
• Design and own end-to-end data pipelines — batch and real-time — handling bid requests, win/loss events, impression logs, click streams, and conversion signals at scale.
• Build and maintain our data lakehouse architecture (ingestion → storage → serving layer) optimized for low-latency DSP analytics and ML feature generation.
• Define standards for data quality, lineage, observability, and SLA adherence across all data products.
• Own and evolve our StarRocks deployment for real-time analytical queries — schema design, ingestion patterns, and query optimisation for DSP reporting workloads.
• Drive FinOps discipline across the data platform — track compute and storage costs, enforce resource quotas, identify optimisation opportunities, and report on unit economics.
• Establish and lead Data Governance practices: metadata management, data cataloguing, access control policies, PII classification, and data retention standards compliant with GDPR and CCPA.
• Backend Microservices
• Contribute to and review the design of backend services that power DSP functionality — bid shading, pacing, budget management, and reporting APIs.
• Architect data contracts and event schemas between microservices, ensuring consistency across the bid request lifecycle.
• Champion best practices in API design, service reliability (SLOs, circuit breakers, retries), and observability (distributed tracing, structured logging, metrics).
• Collaborate with the bidder team on low-latency data reads (Aerospike, Cassandra, or similar) for real-time targeting signal lookups
• Technical Leadership
• Lead architecture reviews, set engineering standards, and drive adoption of best practices across the data and backend engineering teams.
• Mentor SDE II/III engineers through code reviews, design sessions, and pair programming; grow the next generation of technical leaders.
• Partner with ML engineers on the full model lifecycle — feature engineering pipelines, offline training data generation, experiment tracking, and model serving infrastructure.
• Translate complex business requirements (campaign KPIs, ROAS, viewability, brand safety) into robust technical designs.
• Own the technical roadmap for data infrastructure — capacity planning, migration strategies, and build vs. buy decisions.

• 8+ years of software engineering experience, with at least 4 years focused on data engineering or large-scale data systems.
• Prior experience in ad-tech, programmatic advertising, or real-time bidding (RTB) environments strongly preferred.
• Experience in ML engineering, feature engineering, building offline training pipelines, or collaborating closely on model productionisation — is preferred.
• Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.

• Deep expertise in distributed stream processing frameworks — Apache Kafka, Apache Flink, or Apache Spark Structured Streaming.
• Strong command of SQL and experience optimizing complex analytical queries at petabyte scale.
• Hands-on experience with real-time analytical databases, particularly StarRocks; familiarity with Snowflake or ClickHouse is a plus.
• Proficiency with workflow orchestration tools: Apache Airflow, Prefect, or Dagster.
• Solid understanding of data modelling — dimensional modelling, OBT patterns, and eventdriven schemas relevant to ad impression and attribution data.
• Familiarity with open table formats: Apache Iceberg, Delta Lake, or Apache Hudi.

• Proficiency in Python (primary) and/or Java/Scala for building production-grade services and data processing jobs.
• Experience designing and building RESTful APIs and/or gRPC-based microservices.
• Working knowledge of low-latency data stores (Aerospike, Cassandra) used for real-time lookups in the bidding path.
• Comfortable with containerised deployments on Kubernetes and CI/CD pipelines (GitHub Actions, ArgoCD, or similar).

• Demonstrated experience leading technical squads or functioning as a technical anchor on cross-functional projects.
• Strong written and verbal communication skills; ability to translate technical complexity for nonengineering stakeholders.
• Track record of driving projects from ambiguous requirements to production with high engineering quality

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